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Charles Plessy

Publications and source records attributed to Charles Plessy.

2 recordsLinked to original sources

clonotypeR--high throughput analysis of T cell antigen receptor sequences

MotivationThe T cell receptors are expressed as millions of different rearrangements. Amplified as a complex mixture of PCR products, they can be sequenced directly on next-generation instruments without the need for cloning. This method is increasingly used to characterize, quantify and study these highly diverse receptors.\n\nResultsWe present here clonotypeR, a software package to identify and analyze antigen receptors from high-throughput sequence libraries. ClonotypeR is designed to process, organize and analyze very large numbers of sequences, in the order of millions, typically produced by Roche 454 or Illumina instruments, and is made of two parts. The first contains shell scripts and reference segment sequences to produce a data file where each line represents a the detection of a clonotype in a sequence read. The second part is a R module available from Bioconductor, to load and filter the data, and prepare clonotype abundance tables ready for analysis with third-party tools for differential representation analysis, sample clustering, etc. To analyze clonotype data at the nucleotide level, we introduce unique clonotype identifiers based on those developed by Yassai et al. (2009), that we corrected to avoid identifier collisions.\n\nAvailabilityhttp://clonotyper.branchable.com (CC0 license).

Bioinformatics

Targeted reduction of highly abundant transcripts with pseudo-random primers

Transcriptome studies based on quantitative sequencing estimate gene expression levels by measuring the abundance of target RNAs in libraries of sequence reads. The sequencing cost is proportional to the total number of sequenced reads. Therefore, in order to cover rare RNAs, considerable quantities of abundant and identical reads have to be sequenced. This major limitation can be lifted by strategies used to deplete the library from some of the most abundant sequences. However, these strategies involve either an extra handling of the input RNA sample, or the use of a large number of reverse-transcription primers (termed \"not-so-random primers\"), which are costly to synthetize and customize. Here, we demonstrate that with a precise selection of only 40 \"pseudo-random\" reverse-transcription primers, it is possible to decrease the rate of undesirable abundant sequences within a library without affecting the transcriptome diversity. \"Pseudo-random\" primers are simple to design, and therefore are a flexible tool for enriching transcriptome libraries in rare transcripts sequences.

Molecular Biology