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Decipher sequencing data with file formats for NGS

Next-generation sequencing (NGS) technologies generate tremendous amount of data. A typical human genome consists of ~3 billion base pairs to be sequenced. A critical step in NGS is to extract the information, stock it and transmit it in an easy-to-use and lightweight way. Why need file format? Coded in bits, a human genome would “only” weight about 700 MB (Reid Robison). However, sequencers generate short reads that redundantly span the sequence and then need to be aligned to a reference genome. Moreover, since sequencing is not perfect, every base has a score attached to it to evaluate the quality of sequencing. Thus, file formats have been developed to code a maximum of this information in a minimum of space. Here …

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Evaluating the functional impact of genetic variants with COPE software

Dr Ge Gao, developer of the COPE-PCG software tool, talks here about his tool and how it can assist researchers to analyze sequencing data. COPE: A new framework of context-oriented prediction for variant effects Evaluating functional impacts of genetic variants is a key step in genomic studies. Whilst most popular variant annotation tools take a variant-centric strategy and assess the functional consequence of each variant independently, multiple variants in the same gene may interfere with each other and have different effects in combination than individually (e.g., a frameshift caused by an indel can be “rescued” by another downstream variant). The COPE framework, Context-Oriented Predictor for variant Effect, was developed to accurately annotate multiple co-occurring variants. This new gene-centric annotation tool …

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Guidelines for RNA-sequencing data analysis

Adaptation and translation by Sarah Mackenzie and Helene Perrin of the french articles on bioinfo-fr.net: « L’analyse de données RNA-seq: mode d’emploi » by Julien Delafontaine, Bioinformatician at Lausanne University, and « RNA-seq : plus de profondeur ou plus d’échantillons? » by Isabelle Stévant, PhD student in Bioinformatics at Geneva University.   High-throughput sequencing is currently producing massive quantities of data, which is often extremely difficult for biologists to analyze, and who thus look to bioinformaticians for help. From these tens of gigabytes of data, the biologist hopes to extract results which are statistically relevant (preferably with very small p-values!), biologically interpretable, and which justify the time and money invested in the study.   A biologist typically has multiple aims, such as: to …

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