Robust detection of alternative splicing in a population of single cells

Single cell RNA-seq experiments provide valuable insight into cellular heterogeneity but suffer from low coverage, 3′ bias and technical noise. These unique properties of single cell RNA-seq data make study of alternative splicing difficult, and thus most single cell studies have restricted analysis of transcriptome variation to the gene level. To address these limitations, we developed SingleSplice, which uses a statistical model to detect genes whose isoform usage shows biological variation significantly exceeding technical noise in a population of single cells. Importantly, SingleSplice is tailored to the unique demands of single cell analysis, detecting isoform usage differences without attempting to infer expression levels for full-length transcripts. Using data from spike-in transcripts, we found that our approach detects variation in isoform usage among single cells with high sensitivity and specificity. We also applied SingleSplice to data from mouse embryonic stem cells and discovered a set of genes that show significant biological variation in isoform usage across the set of cells. A subset of these isoform differences are linked to cell cycle stage, suggesting a novel connection between alternative splicing and the cell cycle

SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data

Accuracy of trajectory reconstruction using a subset of cells. (a) Graph showing how similar the SLICER trajectory is when computed using a random subset of lung cells. The blue bars show the similarity in cell ordering (units are percent sorted with respect to the trajectory constructed from all cells). The orange bars show the similarity in branch assignments (percentage of cells assigned to the same branch as the trajectory constructed from all cells). The values shown were obtained by averaging the results from five subsampled datasets for each percentage (80 %, 60 %, 40 %, and 20 %). (b) Order preservation and branch identity values computed as in panel (a), but for datasets sampled from the neural stem cell dataset. (PDF 106 kb

Prognostic factors for important clinical outcomes in patients with a severe infection

Patients who are admitted with a suspicion of a severe infection usually enter the hospital through the emergency department (ED). The recognition of prognostic factors in an early stage affects further treatment and might improve clinical outcomes. WE EXAMINED POSSIBLE PROGNOSTIC FACTORS FOR FOUR IMPORTANT OUTCOMES: intensive care unit (ICU) admission, positive blood cultures, mortality and re-admission. All adult patients arriving at the ED with a suspected infection for whom admittance and intravenous (iv) antibiotics were indicated were included between March and December 2006. Possible prognostic variables were obtained from medical history, physical examination and laboratory results during the ED presentation. Data were analysed using logistic regression analysis. A total of 295 ED patients were evaluated, of whom 27 were referred to the ICU, 62 had a positive blood culture, 16 died and 48 were re-admitted. In multivariate analysis, patients with a respiration rate of >25/min were at higher risk for ICU admission. Patients with a positive blood culture had a higher heart rate and a higher percentage of segmented neutrophils. Patients who died during admission were more likely to be older, confused and had lower blood pressure. Patients who were re-admitted within 30 days were more likely to be male, younger and less likely to have a positive blood culture. Routine clinical and biochemical information can be used to predict ICU admission, the presence of bacteraemia, mortality and re-admission (within 30 days) and should be taken into consideration for treatment decision

BlackOPs: Increasing confidence in variant detection through mappability filtering

Identifying variants using high-throughput sequen-cing data is currently a challenge because true biological variants can be indistinguishable from technical artifacts. One source of technical arti-fact results from incorrectly aligning experimen-tally observed sequences to their true genomic origin (‘mismapping’) and inferring differences in mismapped sequences to be true variants. We de-veloped BlackOPs, an open-source tool that simu-lates experimental RNA-seq and DNA whole exome sequences derived from the reference genome, aligns these sequences by custom parameters, detects variants and outputs a blacklist of positions and alleles caused by mismapping. Blacklist

The development of research supervisors’ pedagogical content knowledge in a lesson study project

In this study, we aimed to identify how the learning activities elicited in a lesson study project contributed to self-perceived change in supervisors’ pedagogical content knowledge (PCK). Lesson study is a method which combines both professional and educational development. During a lesson study project, teachers collaborate in a team and develop, teach, evaluate, and redesign a research lesson. During the 4-month lesson study project described here, four supervisors designed a protocol for research supervision meetings aimed at enhancing undergraduate students’ learning. During the project, they experimented with open questioning and giving positive feedback instead of giving instruction and explanations. A mixed-methods design was used in this study. Data on the supervisors’ learning activities and PCK were gathered using learner reports, video-recordings of meetings, and exit interviews. The analyses of these data showed that the lesson study project contributed to the development of the supervisors’ PCK on instructional strategies and student understanding. The learning activity that contributed most to these changes was reflecting on their own practice and that of their students

Pseudogenes transcribed in breast invasive carcinoma show subtype-specific expression and ceRNA potential

BackgroundRecent studies have shown that some pseudogenes are transcribed and contribute to cancer when dysregulated. In particular, pseudogene transcripts can function as competing endogenous RNAs (ceRNAs). The high similarity of gene and pseudogene nucleotide sequence has hindered experimental investigation of these mechanisms using RNA-seq. Furthermore, previous studies of pseudogenes in breast cancer have not integrated miRNA expression data in order to perform large-scale analysis of ceRNA potential. Thus, knowledge of both pseudogene ceRNA function and the role of pseudogene expression in cancer are restricted to isolated examples.ResultsTo investigate whether transcribed pseudogenes play a pervasive regulatory role in cancer, we developed a novel bioinformatic method for measuring pseudogene transcription from RNA-seq data. We applied this method to 819 breast cancer samples from The Cancer Genome Atlas (TCGA) project. We then clustered the samples using pseudogene expression levels and integrated sample-paired pseudogene, gene and miRNA expression data with miRNA target prediction to determine whether more pseudogenes have ceRNA potential than expected by chance.ConclusionsOur analysis identifies with high confidence a set of 440 pseudogenes that are transcribed in breast cancer tissue. Of this set, 309 pseudogenes exhibit significant differential expression among breast cancer subtypes. Hierarchical clustering using only pseudogene expression levels accurately separates tumor samples from normal samples and discriminates the Basal subtype from the Luminal and Her2 subtypes. Correlation analysis shows more positively correlated pseudogene-parent gene pairs and negatively correlated pseudogene-miRNA pairs than expected by chance. Furthermore, 177 transcribed pseudogenes possess binding sites for co-expressed miRNAs that are also predicted to target their parent genes. Taken together, these results increase the catalog of putative pseudogene ceRNAs and suggest that pseudogene transcription in breast cancer may play a larger role than previously appreciated.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1227-8) contains supplementary material, which is available to authorized users

Mouse chromosome 3

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47004/1/335_2004_Article_BF00360829.pd

EnD-Seq and AppEnD: sequencing 3′ ends to identify nontemplated tails and degradation intermediates

Existing methods for detecting RNA intermediates resulting from exonuclease degradation are low-throughput and laborious. In addition, mapping the 3′ ends of RNA molecules to the genome after high-throughput sequencing is challenging, particularly if the 3′ ends contain post-transcriptional modifications. To address these problems, we developed EnD-Seq, a high-throughput sequencing protocol that preserves the 3′ end of RNA molecules, and AppEnD, a computational method for analyzing high-throughput sequencing data. Together these allow determination of the 3′ ends of RNA molecules, including nontemplated additions. Applying EnD-Seq and AppEnD to histone mRNAs revealed that a significant fraction of cytoplasmic histone mRNAs end in one or two uridines, which have replaced the 1–2 nt at the 3′ end of mature histone mRNA maintaining the length of the histone transcripts. Histone mRNAs in fly embryos and ovaries show the same pattern, but with different tail nucleotide compositions. We increase the sensitivity of EnD-Seq by using cDNA priming to specifically enrich low-abundance tails of known sequence composition allowing identification of degradation intermediates. In addition, we show the broad applicability of our computational approach by using AppEnD to gain insight into 3′ additions from diverse types of sequencing data, including data from small capped RNA sequencing and some alternative polyadenylation protocols

Selective single cell isolation for genomics using microraft arrays

Genomic methods are used increasingly to interrogate the individual cells that compose specific tissues. However, current methods for single cell isolation struggle to phenotypically differentiate specific cells in a heterogeneous population and rely primarily on the use of fluorescent markers. Many cellular phenotypes of interest are too complex to be measured by this approach, making it difficult to connect genotype and phenotype at the level of individual cells. Here we demonstrate that microraft arrays, which are arrays containing thousands of individual cell culture sites, can be used to select single cells based on a variety of phenotypes, such as cell surface markers, cell proliferation and drug response. We then show that a common genomic procedure, RNA-seq, can be readily adapted to the single cells isolated from these rafts. We show that data generated using microrafts and our modified RNA-seq protocol compared favorably with the Fluidigm C1. We then used microraft arrays to select pancreatic cancer cells that proliferate in spite of cytotoxic drug treatment. Our single cell RNA-seq data identified several expected and novel gene expression changes associated with early drug resistance