bioRxiv Science⌕ Search

Biology subjects

Darmon, S.

Publications and source records attributed to Darmon, S..

2 recordsLinked to original sources

Extended t-cores for the de novo identification of transposable elements and other inexact repeats from short read RNAseq data

Transcribed repeats represent a major challenge in the de novo assembly of transcriptomes from short RNA-seq reads. Young transposable elements (TEs) and more generally, inexact repeats, create dense and ambiguous regions in the assembly graph, preventing the correct assembly of transcripts. In this paper, we introduce a fully de novo method based on the discovery of dense regions in the compacted De Bruijn graph (DBG) to identify such repeats directly from short read RNA-seq data, without requiring a reference genome or repeat database. Our approach defines the extended t-cores, subgraphs of the DBG that capture the complex topology induced by expressed inexact repeats appearing in RNA-seq reads. Independently of its interest for transcriptome assembly, the proposed method appears to be effective for the de novo identification of repeats in transcriptomes. After classifying cores using sequence-based motifs to distinguish simple repeats from potential TEs, we demonstrate its potential for the de novo discovery of TEs. We validate the approach on a Mus musculus dataset, showing that extended t-cores correspond to known expressed TE families. We also illustrate its de novo discovery potential on a non-model species, Canis lupus familiaris, where the method was also able to recover known TEs.

bioinformatics↗

Frugal alignment-free identification of FLT3-internal tandem duplications with FiLT3r

BackgroundInternal tandem duplications in the FLT3 gene, termed FLT3-ITDs, are useful molecular markers in acute myeloid leukemia (AML) for patient risk stratification and follow-up. FLT3-ITDs are increasingly screened through high-throughput sequencing (HTS) raising the need for robust and efficient algorithms. We developed a new algorithm, which performs no alignment and uses little resources, to identify and quantify FLT3-ITDs in HTS data. ResultsOur algorithm (FiLT3r) focuses on the k-mers from reads covering FLT3 exons 14 and 15. We show that those k-mers bring enough information to accurately detect, determine the length and quantify FLT3-ITD duplications. We compare the performances of FiLT3r to state-of-the-art alternatives and to fragment analysis, the gold standard method, on a cohort of 185 AML patients sequenced with capture-based HTS. On this dataset FiLT3r is more precise (no false positive nor false negative) than the other software evaluated. We also assess the software on public RNA-Seq data, which confirms the previous results and shows that FiLT3r requires little resources compared to other software. ConclusionFiLT3r is a free software available at https://gitlab.univ-lille.fr/filt3r/filt3r. The repository also contains a Snakefile to reproduce our experiments. We show that FiLT3r detects FLT3-ITDs better than other software while using less memory and time.

bioinformatics↗