共查询到20条相似文献,搜索用时 15 毫秒
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High-throughput DNA sequencing (HTS) is of increasing importance in the life sciences. One of its most prominent applications is the sequencing of whole genomes or targeted regions of the genome such as all exonic regions (i.e., the exome). Here, the objective is the identification of genetic variants such as single nucleotide polymorphisms (SNPs). The extraction of SNPs from the raw genetic sequences involves many processing steps and the application of a diverse set of tools. We review the essential building blocks for a pipeline that calls SNPs from raw HTS data. The pipeline includes quality control, mapping of short reads to the reference genome, visualization and post-processing of the alignment including base quality recalibration. The final steps of the pipeline include the SNP calling procedure along with filtering of SNP candidates. The steps of this pipeline are accompanied by an analysis of a publicly available whole-exome sequencing dataset. To this end, we employ several alignment programs and SNP calling routines for highlighting the fact that the choice of the tools significantly affects the final results. 相似文献
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Accurate mapping of spliced RNA-Seq reads to genomic DNA has been known as a challenging problem. Despite significant efforts invested in developing efficient algorithms, with the human genome as a primary focus, the best solution is still not known. A recently introduced tool, TrueSight, has demonstrated better performance compared with earlier developed algorithms such as TopHat and MapSplice. To improve detection of splice junctions, TrueSight uses information on statistical patterns of nucleotide ordering in intronic and exonic DNA. This line of research led to yet another new algorithm, UnSplicer, designed for eukaryotic species with compact genomes where functional alternative splicing is likely to be dominated by splicing noise. Genome-specific parameters of the new algorithm are generated by GeneMark-ES, an ab initio gene prediction algorithm based on unsupervised training. UnSplicer shares several components with TrueSight; the difference lies in the training strategy and the classification algorithm. We tested UnSplicer on RNA-Seq data sets of Arabidopsis thaliana, Caenorhabditis elegans, Cryptococcus neoformans and Drosophila melanogaster. We have shown that splice junctions inferred by UnSplicer are in better agreement with knowledge accumulated on these well-studied genomes than predictions made by earlier developed tools. 相似文献
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Sailakshmi Subramanian Valentina Di Pierro Hardik Shah Anitha D. Jayaprakash Ian Weisberger Jaehee Shim Ajish George Bruce D. Gelb Ravi Sachidanandam 《Nucleic acids research》2013,41(16):e154
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. 相似文献
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Mapping and quantifying mammalian transcriptomes by RNA-Seq 总被引:43,自引:0,他引:43
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Polymorphism and variations in gene expression provide the genetic basis for human variation. Allelic variation of gene expression, in particular, may play a crucial role in phenotypic variation and disease susceptibility. To identify genes with allelic expression in human cells, we genotyped genomic DNA and cDNA isolated from 31 immortalized B cell lines from three Centre d'Etude du Polymorphisme Humain (CEPH) families using high-density single-nucleotide polymorphism (SNP) chips containing 13,900 exonic SNPs. We identified seven SNPs in five genes with monoallelic expression, 146 SNPs in 125 genes with allelic imbalance in expression with preferentially higher expression of one allele in a heterozygous individual. The monoallelically expressed genes (ERAP2, MDGA1, LOC644422, SDCCAG3P1 and CLTCL1) were regulated by cis-acting, non-imprinted differential allelic control. In addition, all monoallelic gene expression patterns and allelic imbalances in gene expression in B cells were transmitted from parents to offspring in the pedigree, indicating genetic transmission of allelic gene expression. Furthermore, frequent allele substitution, probably due to RNA editing, was also observed in 21 genes in 23 SNPs as well as in 48 SNPs located in regions containing no known genes. In this study, we demonstrated that allelic gene expression is frequently observed in human B cells, and SNP chips are very useful tools for detecting allelic gene expression. Overall, our data provide a valuable framework for better understanding allelic gene expression in human B cells. 相似文献
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《Genomics》2021,113(6):3864-3871
RNA editing exerts critical impacts on numerous biological processes. While millions of RNA editings have been identified in humans, much more are expected to be discovered. In this work, we constructed Convolutional Neural Network (CNN) models to predict human RNA editing events in both Alu regions and non-Alu regions. With a validation dataset resulting from CRISPR/Cas9 knockout of the ADAR1 enzyme, the validation accuracies reached 99.5% and 93.6% for Alu and non-Alu regions, respectively. We ported our CNN models in a web service named EditPredict. EditPredict not only works on reference genome sequences but can also take into consideration single nucleotide variants in personal genomes. In addition to the human genome, EditPredict tackles other model organisms including bumblebee, fruitfly, mouse, and squid genomes. EditPredict can be used stand-alone to predict novel RNA editing and it can be used to assist in filtering for candidate RNA editing detected from RNA-Seq data. 相似文献
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Accurate estimation of expression levels from RNA-Seq data entails precise mapping of the sequence reads to a reference genome. Because the standard reference genome contains only one allele at any given locus, reads overlapping polymorphic loci that carry a non-reference allele are at least one mismatch away from the reference and, hence, are less likely to be mapped. This bias in read mapping leads to inaccurate estimates of allele-specific expression (ASE). To address this read-mapping bias, we propose the construction of an enhanced reference genome that includes the alternative alleles at known polymorphic loci. We show that mapping to this enhanced reference reduced the read-mapping biases, leading to more reliable estimates of ASE. Experiments on simulated data show that the proposed strategy reduced the number of loci with mapping bias by ≥63% when compared with a previous approach that relies on masking the polymorphic loci and by ≥18% when compared with the standard approach that uses an unaltered reference. When we applied our strategy to actual RNA-Seq data, we found that it mapped up to 15% more reads than the previous approaches and identified many seemingly incorrect inferences made by them. 相似文献
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Nikolaos I Panousis Maria Gutierrez-Arcelus Emmanouil T Dermitzakis Tuuli Lappalainen 《Genome biology》2014,15(9)