Geoseq: a tool for dissecting deep-sequencing datasets

Gurtowski, James; Cancio, Anthony; Shah, Hardik; Levovitz, Chaya; George, Ajish; Homann, Robert; Sachidanandam, Ravi

doi:10.1186/1471-2105-11-506

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…We used exact matches to map the reads to the zebrafish genome (Zv9) and estimate the coverage of each gene ( Aravin et al, 2007 ; Olson et al, 2008 ). Briefly, the reads were split into three 32 bp pairs after trimming 2 nt at each end, and the parts were mapped to the genome using a suffix-array based approach (detailed in [ Gurtowski et al, 2010 ]). The mappings were then converted to an expression level by using the median of coverage across the transcript as an estimate of gene expression.…”

Section: Methodsmentioning

confidence: 99%

MPI depletion enhances O-GlcNAcylation of p53 and suppresses the Warburg effect

Shtraizent

DeRossi

Nayar

et al. 2017

eLife

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Rapid cellular proliferation in early development and cancer depends on glucose metabolism to fuel macromolecule biosynthesis. Metabolic enzymes are presumed regulators of this glycolysis-driven metabolic program, known as the Warburg effect; however, few have been identified. We uncover a previously unappreciated role for Mannose phosphate isomerase (MPI) as a metabolic enzyme required to maintain Warburg metabolism in zebrafish embryos and in both primary and malignant mammalian cells. The functional consequences of MPI loss are striking: glycolysis is blocked and cells die. These phenotypes are caused by induction of p53 and accumulation of the glycolytic intermediate fructose 6-phosphate, leading to engagement of the hexosamine biosynthetic pathway (HBP), increased O-GlcNAcylation, and p53 stabilization. Inhibiting the HBP through genetic and chemical methods reverses p53 stabilization and rescues the Mpi-deficient phenotype. This work provides mechanistic evidence by which MPI loss induces p53, and identifies MPI as a novel regulator of p53 and Warburg metabolism.DOI: http://dx.doi.org/10.7554/eLife.22477.001

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Section: Methodsmentioning

confidence: 99%

MPI depletion enhances O-GlcNAcylation of p53 and suppresses the Warburg effect

Shtraizent

DeRossi

Nayar

et al. 2017

eLife

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“…This suggests that a fine mapping of reads that map to the shadow can accurately estimate the allele frequencies in the samples. The reads are searched for exact matches to tiles from a chosen exonic region using Geoseq (8). Smaller tiles will allow more mismatches, making it more sensitive but increasing the computational cost for aligning the reads.…”

Section: Methodsmentioning

confidence: 99%

MiST: A new approach to variant detection in deep sequencing datasets

Subramanian

Pierro

Shah

et al. 2013

Nucleic Acids Research

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MiST is a novel approach to variant calling from deep sequencing data, using the inverted mapping approach developed for Geoseq. Reads that can map to a targeted exonic region are identified using exact matches to tiles from the region. The reads are then aligned to the targets to discover variants. MiST carefully handles paralogous reads that map ambiguously to the genome and clonal reads arising from PCR bias, which are the two major sources of errors in variant calling. The reduced computational complexity of mapping selected reads to targeted regions of the genome improves speed, specificity and sensitivity of variant detection. Compared with variant calls from the GATK platform, MiST showed better concordance with SNPs from dbSNP and genotypes determined by an exonic-SNP array. Variant calls made only by MiST confirm at a high rate (>90%) by Sanger sequencing. Thus, MiST is a valuable alternative tool to analyse variants in deep sequencing data.

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“…Its newest version, miRDeep2 [6], reaches the accuracy around 98.6%-99.9%. Additionally, several tools or web servers were used to identify novel miRNAs or detect miRNA expression levels via NGS such as deepBase [7], Geoseq [8], miRanalyzer [9], SeqBuster [10], mirTools [11], DSAP [12], miRNAkey [13] and miRExpress [14]. …”

Section: Introductionmentioning

confidence: 99%

A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing

et al. 2013

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Background MicroRNAs (miRNAs) play a critical role in down-regulating gene expression. By coupling with Argonaute family proteins, miRNAs bind to target sites on mRNAs and employ translational repression. A large amount of miRNA-target interactions (MTIs) have been identified by the crosslinking and immunoprecipitation (CLIP) and the photoactivatable-ribonucleoside-enhanced CLIP (PAR-CLIP) along with the next-generation sequencing (NGS). PAR-CLIP shows high efficiency of RNA co-immunoprecipitation, but it also lead to T to C conversion in miRNA-RNA-protein crosslinking regions. This artificial error obviously reduces the mappability of reads. However, a specific tool to analyze CLIP and PAR-CLIP data that takes T to C conversion into account is still in need. Results We herein propose the first CLIP and PAR-CLIP sequencing analysis platform specifically for miRNA target analysis, namely miRTarCLIP. From scratch, it automatically removes adaptor sequences from raw reads, filters low quality reads, reverts C to T, aligns reads to 3'UTRs, scans for read clusters, identifies high confidence miRNA target sites, and provides annotations from external databases. With multi-threading techniques and our novel C to T reversion procedure, miRTarCLIP greatly reduces the running time comparing to conventional approaches. In addition, miRTarCLIP serves with a web-based interface to provide better user experiences in browsing and searching targets of interested miRNAs. To demonstrate the superior functionality of miRTarCLIP, we applied miRTarCLIP to two public available CLIP and PAR-CLIP sequencing datasets. miRTarCLIP not only shows comparable results to that of other existing tools in a much faster speed, but also reveals interesting features among these putative target sites. Specifically, we used miRTarCLIP to disclose that T to C conversion within position 1-7 and that within position 8-14 of miRNA target sites are significantly different (p value = 0.02), and even more significant when focusing on sites targeted by top 102 highly expressed miRNAs only (p value = 0.01). These results comply with previous findings and further suggest that combining miRNA expression and PAR-CLIP data can improve accuracy of the miRNA target prediction. Conclusion To sum up, we devised a systematic approach for mining miRNA-target sites from CLIP-seq and PAR-CLIP sequencing data, and integrated the workflow with a graphical web-based browser, which provides a user friendly interface and detailed annotations of MTIs. We also showed through real-life examples that miRTarCLIP is a powerful tool for understanding miRNAs. Our integrated tool can be accessed online freely at http://miRTarCLIP.mbc.nctu.edu.tw.

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Geoseq: a tool for dissecting deep-sequencing datasets

Cited by 4 publications

References 16 publications

MPI depletion enhances O-GlcNAcylation of p53 and suppresses the Warburg effect

MPI depletion enhances O-GlcNAcylation of p53 and suppresses the Warburg effect

MiST: A new approach to variant detection in deep sequencing datasets

A computational approach for identifying microRNA-target interactions using high-throughput CLIP and PAR-CLIP sequencing

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