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Massively parallel RNA sequencing (RNA-seq) has rapidly become the assay of choice for interrogating RNA transcript abundance and diversity. This article provides a detailed introduction to fundamental RNA-seq molecular biology and informatics concepts. We make available open-access RNA-seq tutorials that cover cloud computing, tool installation, relevant file formats, reference genomes, transcriptome annotations, quality-control strategies, expression, differential expression, and alternative splicing analysis methods. These tutorials and additional training resources are accompanied by complete analysis pipelines and test datasets made available without encumbrance at www.rnaseq.wiki.

Efficient tools for data management and integration are essential for many aspects of high-throughput biology. In particular, annotations of genes and human genetic variants are commonly used but highly fragmented across many resources. Here, we describe MyGene.info and MyVariant.info, high-performance web services for querying gene and variant annotation information. These web services are currently accessed more than three million times per month. They also demonstrate a generalizable cloud-based model for organizing and querying biological annotation information. MyGene.info and MyVariant.info are provided as high-performance web services, accessible at http://mygene.info and http://myvariant.info . Both are offered free of charge to the research community.

Large-scale cancer genomics discovery projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have systematically characterized the molecular lesions in human cancer genomes, thereby laying the foundation for precision cancer medicine. However, a curated set of somatic variants with established relevance to cancer biology is essential for clinical annotation and for use in computational data analysis. We have created a database of curated mutations in cancer.

Cerebrospinal fluid (CSF) 42 amino acid species of amyloid beta (Aβ42) and tau levels are strongly correlated with the presence of Alzheimer's disease (AD) neuropathology including amyloid plaques and neurodegeneration and have been successfully used as endophenotypes for genetic studies of AD. Additional CSF analytes may also serve as useful endophenotypes that capture other aspects of AD pathophysiology. Here we have conducted a genome-wide association study of CSF levels of 59 AD-related analytes. All analytes were measured using the Rules Based Medicine Human DiscoveryMAP Panel, which includes analytes relevant to several disease-related processes. Data from two independently collected and measured datasets, the Knight Alzheimer's Disease Research Center (ADRC) and Alzheimer's Disease Neuroimaging Initiative (ADNI), were analyzed separately, and combined results were obtained using meta-analysis. We identified genetic associations with CSF levels of 5 proteins (Angiotensin-converting enzyme (ACE), Chemokine (C-C motif) ligand 2 (CCL2), Chemokine (C-C motif) ligand 4 (CCL4), Interleukin 6 receptor (IL6R) and Matrix metalloproteinase-3 (MMP3)) with study-wide significant p-values (p<1.46×10-10) and significant, consistent evidence for association in both the Knight ADRC and the ADNI samples. These proteins are involved in amyloid processing and pro-inflammatory signaling. SNPs associated with ACE, IL6R and MMP3 protein levels are located within the coding regions of the corresponding structural gene. The SNPs associated with CSF levels of CCL4 and CCL2 are located in known chemokine binding proteins. The genetic associations reported here are novel and suggest mechanisms for genetic control of CSF and plasma levels of these disease-related proteins. Significant SNPs in ACE and MMP3 also showed association with AD risk. Our findings suggest that these proteins/pathways may be valuable therapeutic targets for AD. Robust associations in cognitively normal individuals suggest that these SNPs also influence regulation of these proteins more generally and may therefore be relevant to other diseases.

CIViC is an expert-crowdsourced knowledgebase for Clinical Interpretation of Variants in Cancer describing the therapeutic, prognostic, diagnostic and predisposing relevance of inherited and somatic variants of all types. CIViC is committed to open-source code, open-access content, public application programming interfaces (APIs) and provenance of supporting evidence to allow for the transparent creation of current and accurate variant interpretations for use in cancer precision medicine.

A study of genome evolution in a metastatic breast cancer bearing an activating PIK3CA mutation, following treatment with the PI(3)Kα inhibitor BYL719, shows that all metastatic lesions, when compared to the pre-treatment tumour, had lost a copy of PTEN; parallel genetic evolution of separate sites with different PTEN genomic alterations had led to a convergent PTEN-null phenotype resistant to PI(3)Kα inhibition. Anticancer resistance due to PTEN loss The emergence and expansion of resistant clonal subpopulations is a major challenge facing the field of targeted therapeutics. Here, the authors study genome evolution in a metastatic breast cancer bearing an activating PIK3CA mutation, following treatment with the phosphatidylinositol-4,5-bisphosphate 3-kinase alpha subunit (PI(3)Kα) selective-inhibitor BYL719. Fourteen metastatic sites were sequenced to reveal that all metastatic lesions, when compared to the pre-treatment tumour, had lost a copy of the gene for the tumour suppressor phosphatase PTEN . Resistance to BYL719 was associated with additional and different PTEN genetic alterations, resulting in loss of PTEN expression. Based on these observations and additional functional characterization, the authors conclude that parallel genetic evolution of separate sites with different PTEN genomic alterations leads to a convergent PTEN-null phenotype resistant to PI(3)Kα inhibition. Broad and deep tumour genome sequencing has shed new light on tumour heterogeneity and provided important insights into the evolution of metastases arising from different clones 1 , 2 . There is an additional layer of complexity, in that tumour evolution may be influenced by selective pressure provided by therapy, in a similar fashion to that occurring in infectious diseases. Here we studied tumour genomic evolution in a patient (index patient) with metastatic breast cancer bearing an activating PIK3CA (phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha, PI(3)Kα) mutation. The patient was treated with the PI(3)Kα inhibitor BYL719, which achieved a lasting clinical response, but the patient eventually became resistant to this drug (emergence of lung metastases) and died shortly thereafter. A rapid autopsy was performed and material from a total of 14 metastatic sites was collected and sequenced. All metastatic lesions, when compared to the pre-treatment tumour, had a copy loss of PTEN (phosphatase and tensin homolog) and those lesions that became refractory to BYL719 had additional and different PTEN genetic alterations, resulting in the loss of PTEN expression. To put these results in context, we examined six other patients also treated with BYL719. Acquired bi-allelic loss of PTEN was found in one of these patients, whereas in two others PIK3CA mutations present in the primary tumour were no longer detected at the time of progression. To characterize our findings functionally, we examined the effects of PTEN knockdown in several preclinical models (both in cell lines intrinsically sensitive to BYL719 and in PTEN -null xenografts derived from our index patient), which we found resulted in resistance to BYL719, whereas simultaneous PI(3)K p110β blockade reverted this resistance phenotype. We conclude that parallel genetic evolution of separate metastatic sites with different PTEN genomic alterations leads to a convergent PTEN-null phenotype resistant to PI(3)Kα inhibition.

Here we report targeted sequencing of 83 genes using DNA from primary breast cancer samples from 625 postmenopausal (UBC-TAM series) and 328 premenopausal (MA12 trial) hormone receptor-positive (HR+) patients to determine interactions between somatic mutation and prognosis. Independent validation of prognostic interactions was achieved using data from the METABRIC study. Previously established associations between MAP3K1 and PIK3CA mutations with luminal A status/favorable prognosis and TP53 mutations with Luminal B/non-luminal tumors/poor prognosis were observed, validating the methodological approach. In UBC-TAM, NF1 frame-shift nonsense (FS/NS) mutations were also a poor outcome driver that was validated in METABRIC. For MA12, poor outcome associated with PIK3R1 mutation was also reproducible. DDR1 mutations were strongly associated with poor prognosis in UBC-TAM despite stringent false discovery correction ( q  = 0.0003). In conclusion, uncommon recurrent somatic mutations should be further explored to create a more complete explanation of the highly variable outcomes that typifies ER+ breast cancer. Unravelling the link between somatic mutation and prognosis in estrogen positive (ER+) breast cancer requires the use of long-term follow-up data. Here, combining archival formalin-fixed paraffin embedded tissue and targeted sequencing in three cohorts of ER+ breast cancer, the authors find associations with clinical outcome for NF1 frame-shift nonsense mutations, PIK3R1 mutation, and DDR1 mutations.

Cancer genomic analysis requires accurate identification of somatic variants in sequencing data. Manual review to refine somatic variant calls is required as a final step after automated processing. However, manual variant refinement is time-consuming, costly, poorly standardized, and non-reproducible. Here, we systematized and standardized somatic variant refinement using a machine learning approach. The final model incorporates 41,000 variants from 440 sequencing cases. This model accurately recapitulated manual refinement labels for three independent testing sets (13,579 variants) and accurately predicted somatic variants confirmed by orthogonal validation sequencing data (212,158 variants). The model improves on manual somatic refinement by reducing bias on calls otherwise subject to high inter-reviewer variability. A machine learning approach for refinement of somatic variant calls automates this process and reduces bias stemming from inter-reviewer variability.

Purpose Following automated variant calling, manual review of aligned read sequences is required to identify a high-quality list of somatic variants. Despite widespread use in analyzing sequence data, methods to standardize manual review have not been described, resulting in high inter- and intralab variability. Methods This manual review standard operating procedure (SOP) consists of methods to annotate variants with four different calls and 19 tags. The calls indicate a reviewer’s confidence in each variant and the tags indicate commonly observed sequencing patterns and artifacts that inform the manual review call. Four individuals were asked to classify variants prior to, and after, reading the SOP and accuracy was assessed by comparing reviewer calls with orthogonal validation sequencing. Results After reading the SOP, average accuracy in somatic variant identification increased by 16.7% ( p value = 0.0298) and average interreviewer agreement increased by 12.7% ( p value < 0.001). Manual review conducted after reading the SOP did not significantly increase reviewer time. Conclusion This SOP supports and enhances manual somatic variant detection by improving reviewer accuracy while reducing the interreviewer variability for variant calling and annotation.

As guidelines, therapies and literature on cancer variants expand, the lack of consensus variant interpretations impedes clinical applications. CIViC is a public-domain, crowd-sourced and adaptable knowledgebase of evidence for the clinical interpretation of variants in cancer, designed to reduce barriers to knowledge sharing and alleviate the variant-interpretation bottleneck.