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Decoding of superimposed traces produced by direct sequencing of heterozygous indels.

ABSTRACT: Direct Sanger sequencing of a diploid template containing a heterozygous insertion or deletion results in a difficult-to-interpret mixed trace formed by two allelic traces superimposed onto each other. Existing computational methods for deconvolution of such traces require knowledge of a reference sequence or the availability of both direct and reverse mixed sequences of the same template. We describe a simple yet accurate method, which uses dynamic programming optimization to predict superimposed allelic sequences solely from a string of letters representing peaks within an individual mixed trace. We used the method to decode 104 human traces (mean length 294 bp) containing heterozygous indels 5 to 30 bp with a mean of 99.1% bases per allelic sequence reconstructed correctly and unambiguously. Simulations with artificial sequences have demonstrated that the method yields accurate reconstructions when (1) the allelic sequences forming the mixed trace are sufficiently similar, (2) the analyzed fragment is significantly longer than the indel, and (3) multiple indels, if present, are well-spaced. Because these conditions occur in most encountered DNA sequences, the method is widely applicable. It is available as a free Web application Indelligent at http://ctap.inhs.uiuc.edu/dmitriev/indel.asp.

SUBMITTER: Dmitriev DA

PROVIDER: S-EPMC2429969 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The emergence of exome sequencing in recent years has enabled rapid and cost-effective detection of genetic variants in coding regions and offers a great opportunity to combine sequencing experiments with subsequent computational analysis for dissecting genetic basis of human inherited diseases. However, this strategy, though successful in practice, still faces such challenges as limited sample size and substantial number or diversity of candidate variants. To overcome these obstacles, researchers have been concentrated in the development of advanced computational methods and have recently achieved great progress for analysing single nucleotide variant. Nevertheless, it still remains unclear on how to analyse indels, another type of genetic variant that accounts for substantial proportion of known disease-causing variants. In this paper, we proposed an integrative method to effectively identify disease-causing indels from exome sequencing data. Specifically, we put forward a statistical method to combine five functional prediction scores, four genic association scores and a genic intolerance score to produce an integrated p-value, which could then be used for prioritizing candidate indels. We performed extensive simulation studies and demonstrated that our method achieved high accuracy in uncovering disease-causing indels. Our software is available at http://bioinfo.au.tsinghua.edu.cn/jianglab/IndelPrioritizer/.

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Decoding breast cancer tissue-stroma interactions using species-specific sequencing.

Project description:IntroductionDecoding transcriptional effects of experimental tissue-tissue or cell-cell interactions is important; for example, to better understand tumor-stroma interactions after transplantation of human cells into mouse (xenografting). Transcriptome analysis of intermixed human and mouse cells has, however, frequently relied on the need to separate the two cell populations prior to transcriptome analysis, which introduces confounding effects on gene expression.MethodsTo circumvent this problem, we here describe a bioinformatics-based, genome-wide transcriptome analysis technique, which allows the human and mouse transcriptomes to be decoded from a mixed mouse and human cell population. The technique is based on a bioinformatic separation of the mouse and human transcriptomes from the initial mixed-species transcriptome resulting from sequencing an excised tumor/stroma specimen without prior cell sorting.ResultsUnder stringent separation criteria, i.e., with a read misassignment frequency of 0.2 %, we show that 99 % of the genes can successfully be assigned to be of mouse or human origin, both in silico, in cultured cells and in vivo. We use a new species-specific sequencing technology-referred to as S(3) ("S-cube")-to provide new insights into the Notch downstream response following Notch ligand-stimulation and to explore transcriptional changes following transplantation of two different breast cancer cell lines (luminal MCF7 and basal-type MDA-MB-231) into mammary fat pad tissue in mice of different immunological status. We find that MCF7 and MDA-MB-231 respond differently to fat pad xenografting and the stromal response to transplantation of MCF7 and MDA-MB-231 cells was also distinct.ConclusionsIn conclusion, the data show that the S(3) technology allows for faithful recording of transcriptomic changes when human and mouse cells are intermixed and that it can be applied to address a broad spectrum of research questions.

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