Michael I. Love

MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens.

We propose the Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) method for prioritizing single-guide RNAs, genes and pathways in genome-scale CRISPR/Cas9 knockout screens. MAGeCK demonstrates better performance compared with existing methods, identifies both positively and negatively selected genes simultaneously, and reports robust results across different experimental conditions. Using public datasets, MAGeCK identified novel essential genes and pathways, including EGFR in vemurafenib-treated A375 cells harboring a BRAF mutation. MAGeCK also detected cell type-specific essential genes, including BCR and ABL1, in KBM7 cells bearing a BCR-ABL fusion, and IGF1R in HL-60 cells, which depends on the insulin signaling pathway for proliferation.

Airway Epithelial miRNA Expression Is Altered in Asthma

RATIONALE Changes in airway epithelial cell differentiation, driven in part by IL-13, are important in asthma. Micro-RNAs (miRNAs) regulate cell differentiation in many systems and could contribute to epithelial abnormalities in asthma. OBJECTIVES To determine whether airway epithelial miRNA expression is altered in asthma and identify IL-13-regulated miRNAs. METHODS We used miRNA microarrays to analyze bronchial epithelial brushings from 16 steroid-naive subjects with asthma before and after inhaled corticosteroids, 19 steroid-using subjects with asthma, and 12 healthy control subjects, and the effects of IL-13 and corticosteroids on cultured bronchial epithelial cells. We used quantitative polymerase chain reaction to confirm selected microarray results. MEASUREMENTS AND MAIN RESULTS Most (12 of 16) steroid-naive subjects with asthma had a markedly abnormal pattern of bronchial epithelial miRNA expression by microarray analysis. Compared with control subjects, 217 miRNAs were differentially expressed in steroid-naive subjects with asthma and 200 in steroid-using subjects with asthma (false discovery rate < 0.05). Treatment with inhaled corticosteroids had modest effects on miRNA expression in steroid-naive asthma, inducing a statistically significant (false discovery rate < 0.05) change for only nine miRNAs. qPCR analysis confirmed differential expression of 22 miRNAs that were highly differentially expressed by microarrays. IL-13 stimulation recapitulated changes in many differentially expressed miRNAs, including four members of the miR-34/449 family, and these changes in miR-34/449 family members were resistant to corticosteroids. CONCLUSIONS Dramatic alterations of airway epithelial cell miRNA levels are a common feature of asthma. These alterations are only modestly corrected by inhaled corticosteroids. IL-13 effects may account for some of these alterations, including repression of miR-34/449 family members that have established roles in airway epithelial cell differentiation. Clinical trial registered with www.clinicaltrials.gov (NCT 00595153).

X-exome sequencing of 405 unresolved families identifies seven novel intellectual disability genes

X-linked intellectual disability (XLID) is a clinically and genetically heterogeneous disorder. During the past two decades in excess of 100 X-chromosome ID genes have been identified. Yet, a large number of families mapping to the X-chromosome remained unresolved suggesting that more XLID genes or loci are yet to be identified. Here, we have investigated 405 unresolved families with XLID. We employed massively parallel sequencing of all X-chromosome exons in the index males. The majority of these males were previously tested negative for copy number variations and for mutations in a subset of known XLID genes by Sanger sequencing. In total, 745 X-chromosomal genes were screened. After stringent filtering, a total of 1297 non-recurrent exonic variants remained for prioritization. Co-segregation analysis of potential clinically relevant changes revealed that 80 families (20%) carried pathogenic variants in established XLID genes. In 19 families, we detected likely causative protein truncating and missense variants in 7 novel and validated XLID genes (CLCN4, CNKSR2, FRMPD4, KLHL15, LAS1L, RLIM and USP27X) and potentially deleterious variants in 2 novel candidate XLID genes (CDK16 and TAF1). We show that the CLCN4 and CNKSR2 variants impair protein functions as indicated by electrophysiological studies and altered differentiation of cultured primary neurons from Clcn4−/− mice or after mRNA knock-down. The newly identified and candidate XLID proteins belong to pathways and networks with established roles in cognitive function and intellectual disability in particular. We suggest that systematic sequencing of all X-chromosomal genes in a cohort of patients with genetic evidence for X-chromosome locus involvement may resolve up to 58% of Fragile X-negative cases.

ChIP-exo signal associated with DNA-binding motifs provides insight into the genomic binding of the glucocorticoid receptor and cooperating transcription factors

The classical DNA recognition sequence of the glucocorticoid receptor (GR) appears to be present at only a fraction of bound genomic regions. To identify sequences responsible for recruitment of this transcription factor (TF) to individual loci, we turned to the high-resolution ChIP-exo approach. We exploited this signal by determining footprint profiles of TF binding at single-base-pair resolution using ExoProfiler, a computational pipeline based on DNA binding motifs. When applied to our GR and the few available public ChIP-exo data sets, we find that ChIP-exo footprints are protein- and recognition sequence-specific signatures of genomic TF association. Furthermore, we show that ChIP-exo captures information about TFs other than the one directly targeted by the antibody in the ChIP procedure. Consequently, the shape of the ChIP-exo footprint can be used to discriminate between direct and indirect (tethering to other DNA-bound proteins) DNA association of GR. Together, our findings indicate that the absence of classical recognition sequences can be explained by direct GR binding to a broader spectrum of sequences than previously known, either as a homodimer or as a heterodimer binding together with a member of the ETS or TEAD families of TFs, or alternatively by indirect recruitment via FOX or STAT proteins. ChIP-exo footprints also bring structural insights and locate DNA:protein cross-link points that are compatible with crystal structures of the studied TFs. Overall, our generically applicable footprint-based approach uncovers new structural and functional insights into the diverse ways of genomic cooperation and association of TFs.

Deletions of chromosomal regulatory boundaries are associated with congenital disease

BackgroundRecent data from genome-wide chromosome conformation capture analysis indicate that the human genome is divided into conserved megabase-sized self-interacting regions called topological domains. These topological domains form the regulatory backbone of the genome and are separated by regulatory boundary elements or barriers. Copy-number variations can potentially alter the topological domain architecture by deleting or duplicating the barriers and thereby allowing enhancers from neighboring domains to ectopically activate genes causing misexpression and disease, a mutational mechanism that has recently been termed enhancer adoption.ResultsWe use the Human Phenotype Ontology database to relate the phenotypes of 922 deletion cases recorded in the DECIPHER database to monogenic diseases associated with genes in or adjacent to the deletions. We identify combinations of tissue-specific enhancers and genes adjacent to the deletion and associated with phenotypes in the corresponding tissue, whereby the phenotype matched that observed in the deletion. We compare this computationally with a gene-dosage pathomechanism that attempts to explain the deletion phenotype based on haploinsufficiency of genes located within the deletions. Up to 11.8% of the deletions could be best explained by enhancer adoption or a combination of enhancer adoption and gene-dosage effects.ConclusionsOur results suggest that enhancer adoption caused by deletions of regulatory boundaries may contribute to a substantial minority of copy-number variation phenotypes and should thus be taken into account in their medical interpretation.

A benchmark for RNA-seq quantification pipelines

Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package (http://bioconductor.org/packages/rnaseqcomp). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest.

Modeling Read Counts for CNV Detection in Exome Sequencing Data

Varying depth of high-throughput sequencing reads along a chromosome makes it possible to observe copy number variants (CNVs) in a sample relative to a reference. In exome and other targeted sequencing projects, technical factors increase variation in read depth while reducing the number of observed locations, adding difficulty to the problem of identifying CNVs. We present a hidden Markov model for detecting CNVs from raw read count data, using background read depth from a control set as well as other positional covariates such as GC-content. The model, exomeCopy, is applied to a large chromosome X exome sequencing project identifying a list of large unique CNVs. CNVs predicted by the model and experimentally validated are then recovered using a cross-platform control set from publicly available exome sequencing data. Simulations show high sensitivity for detecting heterozygous and homozygous CNVs, outperforming normalization and state-of-the-art segmentation methods.

Breakpointer: using local mapping artifacts to support sequence breakpoint discovery from single-end reads.

SUMMARY We developed Breakpointer, a fast algorithm to locate breakpoints of structural variants (SVs) from single-end reads produced by next-generation sequencing. By taking advantage of local non-uniform read distribution and misalignments created by SVs, Breakpointer scans the alignment of single-end reads to identify regions containing potential breakpoints. The detection of such breakpoints can indicate insertions longer than the read length and SVs located in repetitve regions which might be missd by other methods. Thus, Breakpointer complements existing methods to locate SVs from single-end reads. AVAILABILITY https://github.com/ruping/Breakpointer CONTACT ruping@molgen.mpg.de SUPPLEMENTARY INFORMATION Supplementary material is available at Bioinformatics online.

Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation

We find that current computational methods for estimating transcript abundance from RNA-seq data can lead to hundreds of false-positive results. We show that these systematic errors stem largely from a failure to model fragment GC content bias. Sample-specific biases associated with fragment sequence features lead to misidentification of transcript isoforms. We introduce alpine, a method for estimating sample-specific bias-corrected transcript abundance. By incorporating fragment sequence features, alpine greatly increases the accuracy of transcript abundance estimates, enabling a fourfold reduction in the number of false positives for reported changes in expression compared with Cufflinks. Using simulated data, we also show that alpine retains the ability to discover true positives, similar to other approaches. The method is available as an R/Bioconductor package that includes data visualization tools useful for bias discovery.

Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference

Transcript quantication is a central task in the analysis of RNA-seq data. Accurate computational methods for the quantication of transcript abundances are essential for downstream analysis. However, most existing approaches are much slower than is necessary for their degree of accuracy. We introduce Salmon, a novel method and software tool for transcript quantication that exhibits state-of-the-art accuracy while being signicantly faster than most other tools. Salmon achieves this through the combined application of a two-phase inference procedure, a reduced data representation, and a novel lightweight read alignment algorithm. Salmon is written in C++11, and is available under the GPL v3 license as open-source software at https://combine-lab.github.io/salmon.We introduce Salmon, a new method for quantifying transcript abundance from RNA-seq reads that is highly-accurate and very fast. Salmon is the first transcriptome-wide quantifier to model and correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis compared to existing methods that do not account for these biases. Salmon achieves its speed and accuracy by combining a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. These innovations yield both exceptional accuracy and order-of-magnitude speed benefits over alignment-based methods.