Biology subjects

Gautheret, D.

Publications and source records attributed to Gautheret, D..

18 recordsLinked to original sources

bioRxiv2025

Metappuccino: Large Language Model-driven Reconstruction of Sequence Read Archive Metadata for Cancer Research

MotivationHigh-throughput RNA-sequencing has significantly advanced transcriptomic profiling in on-cology. Millions of RNA-seq datasets have accumulated in public databases such as the Sequence Read Archive-SRA. However, fragmented, ambiguous or missing metadata can severely limit accurate cohort selection, introduce bias and delay discoveries. ResultsTo address these issues, we introduce Metappuccino : a metadata enrichment tool based on a fine-tuned Mistral-7B-Instruct large language model with low-rank-adaptation (LoRA). Metappuccino can extract or infer 19 key metadata classes (e.g. organ, disease, cell type) from unstructured text. Fine-tuning was conducted with careful partitioning and training design to preserve the models generalisation capacity, reduce data leakage, and ensure robust, context-aware inference rather than memorisation. When possible, the inferred outputs are mapped to standardised ontologies, such as Cellosaurus, Disease Ontology and Uberon, to produce consistent metadata. As a result, the fine-tuned model achieves significantly improved class prediction accuracy over the base model, performing at least as well as recent large open-source models. Furthermore, it reduces inference time by up to at least two compared to the baseline models. As a pipeline, Metappuccino complements the LLM with well-established Natural Language Processing techniques from the literature to further improve performance. By enriching the metadata of under-annotated sequences, Metappuccino creates greater value from public RNA-seq datasets, with potential applications extending beyond oncology transcriptomics. Availability and ImplementationThe source code of Metappuccino is available on GitHub : github. com/chumphati/Metappuccino. The fine-tuned LLM, MetappuccinoLLModel, is available on Hugging Face : huggingface.co/chumphati/MetappuccinoLLModel. Both repositories are released under Apache-2.0 license. Contactfiona.hak@i2bc.paris-saclay.fr, daniel.gautheret@universite-paris-saclay.fr, melina.gallopin@i2bc.paris-saclay.fr

bioinformatics↗

bioRxiv2025

REINDEER2: practical abundance index at scale

Recent advances in biological sequence indexing have enabled the efficient querying of sequence presence across massive genomic data repositories. While presence queries have become tractable at petabyte scale, retrieving quantitative information such as sequence abundances remains a significant algorithmic challenge. Existing abundance-aware indexes are mostly static, difficult to scale, and often trade off completeness, precision, or updatability. We describe a novel discrete abundance index designed for scalability, dynamic updates, and tunable precision. We combine an inverted index with probabilistic and exact structures to support fast, memory-efficient construction and precise high-throughput queries across thousands of RNA datasets. Our experiments demonstrate that our method REINDEER2 achieves one to two orders of magnitude speedup in construction compared to existing methods, while maintaining comparable or better memory use. Despite using approximate structures for scalability, REINDEER2 achieves sub-1% error on abundance recovery and correlates strongly with reference quantifiers like Kallisto. It also supports sequence-level queries in seconds over thousands of datasets. Code and experiments github.com/Yohan-HernandezCourbevoie/REINDEER2

bioinformatics↗

bioRxiv2025

Deciphering the RNA-based regulation mechanism of the phage-encoded AbiF system in Clostridioides difficile

Clostridioides difficile is the major cause of nosocomial infections associated with antibiotic therapy. The severity of C. difficile infections increased worldwide with the emergence of hypervirulent strains, including 027 ribotype epidemic strains. Many aspects of C. difficiles adaptation strategies during pathogenesis remain poorly understood. This pathogen thrives in gut communities that are rich in microbes and phages. To regulate horizontal transfer of genetic material during its infection cycle, C. difficile relies on diverse mechanisms. More specifically, CRISPR (clustered regularly interspaced short palindromic repeats)-Cas and Toxin-Antitoxin (TA) systems contribute to prophage maintenance, prevention of phage infection, and stress response. Abortive infection (Abi) systems can provide additional lines of anti-phage defense. RNAs have emerged as key components of these systems including CRISPR RNAs and antitoxin RNAs within type I and type III TA. We report here the identification of a new AbiF-like system within a prophage of the hypervirulent C. difficile strain R20291. It is associated with an Abi_2/AbiD/F protein family largely distributed in Bacillota and Pseudomonadota with structural links to ancestral Cas13 proteins at the origin of the RNA-targeting CRISPR-Cas13 systems. We demonstrated toxic activity of the AbiFCd protein in C. difficile and in Escherichia coli and negative regulation of the abiFCd expression by an associated non-coding RNA RCd22. RCd22 contains two conserved abiF motifs and is active both in cis and in trans to neutralize the toxin by direct RNA-protein interaction, similar to RNA antitoxin in type III TA. A mass spectrometry interactomics analysis of protein fractions from MS2-Affinity Purification coupled with RNA sequencing (MAPS) revealed the AbiFCd protein among the most enriched RCd22 partners in C. difficile. Structural modeling of the RNA-protein complex and mutagenesis analysis revealed key positions on both protein and RNA partners for this interaction and toxic activity. In summary, these findings provide valuable insights into the mechanisms of interaction between bacteria and phages, which are pertinent to the advancement of phage therapy, genome editing, epidemiological surveillance, and the formulation of novel therapeutic approaches.

microbiology↗

bioRxiv2024

A specialized bacterial group II intron is a highly efficient retrotransposon

Mobile group II introns are site-specific retrotransposons composed of a large self-splicing ribozyme and an intron-encoded reverse transcriptase that are widespread in bacterial and organellar genomes. Sequence and structural variations of the ribozyme and the associated reverse transcriptase define several lineages of bacterial group II introns. Interestingly, some of these intron families evolved different mobility strategies while others colonize particular genetic contexts. Here, we have investigated the mobility activity of an Escherichia coli group II intron that is inserted into the stop codon of the stress-response gene groEL. Using mobility assays based on over-expression from a donor plasmid, we demonstrate that this intron is a highly efficient and site-specific retrotransposon, capable of colonizing the groEL gene of an E. coli host strain according to the insertion pattern observed in natural genomes. Furthermore, we provide evidence that a chromosomal copy of the full-length retrotransposon can be expressed from its native genetic locus to yield mobile retroelement particles. This intron constitutes a novel model system that could help reveal original mobility strategies used by some group II intron retrotransposons to colonize bacterial genomes.

molecular biology↗

bioRxiv2024

Dual RNA-seq study of the dynamics of coding and non-coding RNAs expression during Clostridioides difficile infection in a mouse model

Clostridioides difficile is the leading cause of healthcare associated diarrhoea in industrialized countries. Many questions remain to be answered about the mechanisms governing its interaction with the host during infection. Non-coding RNAs (ncRNAs) contribute to shaping virulence in many pathogens and modulate host responses, however, their role in C. difficile infection (CDI) has not been explored. To better understand the dynamics of ncRNAs expression contributing to C. difficile infectious cycle and host response, we used a dual RNA- seq approach in a conventional murine model. From the pathogen side, this transcriptomic analysis revealed the upregulation of virulence factors, metabolism and sporulation genes, as well as the identification of 61 ncRNAs differentially expressed during infection that correlated with the analysis of available raw RNA-seq datasets from two independent studies. From these data we identified 118 potential new transcripts in C. difficile including 106 new ncRNA genes. From the host side, we observed the induction of several pro-inflammatory pathways and, among the 185 differentially expressed ncRNAs, the overexpression of microRNAs (miRNAs) previously associated to inflammatory responses or unknown long ncRNAs and miRNAs. A particular host gene expression profile could be associated to the symptomatic infection. In accordance, the metatranscriptomic analysis revealed specific microbiota changes accompanying CDI and specific species associated with symptomatic infection in mice. This first adaptation of in vivo dual RNA-seq to C. difficile contributes to unravelling the regulatory networks involved in C. difficile infectious cycle and host response and provides valuable resources for further studies of RNA-based mechanisms during CDI. ImportanceClostridioides difficile is a major cause of nosocomial infections associated with antibiotic therapy classified as an urgent antibiotic resistance threat. This pathogen interacts with host and gut microbial communities during infection, but the mechanisms of these interactions remain largely to be uncovered. Noncoding RNAs contribute to bacterial virulence and host responses, but their expression has not been explored during C. difficile infection. We took advantage of the conventional mouse model of C. difficile infection to look simultaneously to the dynamics of gene expression in pathogen, its host and gut microbiota composition providing valuable resources for future studies. We identified a number of ncRNAs that could mediate the adaptation of C. difficile inside the host and the crosstalk with the host immune response. Promising inflammation markers and potential therapeutic targets emerged from this work open new directions for RNA-based and microbiota-modulatory strategies to improve the efficiency of C. difficile infection treatments.

microbiology↗

bioRxiv2024

Cell cycle-dependent mRNA localization in P-bodies

Understanding the dynamics of RNA targeting to membraneless organelles is essential to disentangle their functions. Here, we investigate how P-bodies (PBs) evolve during cell cycle progression. PB purification across the cell cycle uncovers widespread changes in their RNA content, which are partly uncoupled from cell cycle-dependent changes in RNA expression. Single molecule FISH shows various mRNA localization patterns in PBs peaking in G1, S, or G2, with examples illustrating the timely capture of mRNAs in PBs when their encoded protein becomes dispensable. Yet, rather than directly reflecting absence of translation, cyclic mRNA localization in PBs can be controlled by RBPs, such as HuR in G2, and by RNA features. Indeed, while PB mRNAs are AU-rich at all cell cycle phases, they are specifically longer in G1, possibly related to post-mitotic PB reassembly. Altogether, our study supports a model where PBs are more than a default location for excess untranslated mRNAs.

cell biology↗

bioRxiv2024

Exploring a large cancer cell line RNA-sequencing dataset with k-mers

Analyzing the immense diversity of RNA isoforms in large RNA-seq repositories requires laborious data processing using specialized tools. Indexing techniques based on k-mers have previously been effective at searching for RNA sequences across thousands of RNA-seq libraries but falling short of enabling direct RNA quantification. We show here that RNAs queried in the form of k-mer sets can be quantified in seconds, with a precision akin to that of conventional RNA quantification methods. We showcase several applications by exploring an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples. Non-reference RNA sequences such as RNAs harboring driver mutations and fusions, splicing isoforms or RNAs derived from repetitive elements, can be retrieved with high accuracy. Moreover, we show that k-mer indexing offers a powerful means to reveal variant RNAs induced by specific gene alterations, for instance in splicing factors. A web server allows public queries in CCLE and other indexes: https://transipedia.fr. Code is provided to allow users to set up their own server from any RNA-seq dataset.

bioinformatics↗

bioRxiv2024

KaMRaT: a C++ toolkit for k-mer count matrix dimension reduction

SummaryKaMRaT is a program for processing large k-mer count tables extracted from high throughput sequencing data. Major functions include scoring k-mers based on count statistics, merging overlapping k-mers into longer contigs and selecting k-mers based on their presence in certain samples. KaMRaT s main application is the reference-free analysis of multi-sample and multi-condition datasets from RNA-seq, as well as ChiP-seq or ribo-seq experiments. KaMRaT enables the identification of condition-specific or differential sequences, irrespective of any gene or transcript annotation. Implementation and availabilityKaMRaT is implemented in C++. Source code and documentation are available via https://github.com/Transipedia/KaMRaT. Container images are available via https://hub.docker.com/r/xuehl/kamrat.

bioinformatics↗

bioRxiv2023

Targeting LINC00152 activates cAMP/Ca2+/ferroptosis axis and overcomes tamoxifen resistance in ER+ breast cancer

Tamoxifen has been the mainstay therapy to treat early, locally advanced, and metastatic estrogen receptor-positive (ER+) breast cancer, constituting around 75% of all cases. However, emergence of resistance is common, necessitating the identification of novel therapeutic targets. Here, we demonstrated that long-noncoding RNA LINC00152 confers tamoxifen resistance via blocking tamoxifen-induced ferroptosis, an iron-mediated cell death. Mechanistically, inhibiting LINC00152 reduces the mRNA stability of phosphodiesterase 4D (PDE4D), leading to activation of cAMP/PKA/CREB axis and increased expression of TRPC1 Ca2+ channel. This causes cytosolic Ca2+ overload and generation of reactive oxygen species (ROS) that is, on one hand, accompanied by downregulation of FTH1, a member of the iron sequestration unit, thus increasing intracellular Fe2+ levels; and on the other hand, inhibition of the peroxidase activity upon reduced GPX4 and xCT levels. These ultimately induce lipid peroxidation and ferroptotic cell death in combination with tamoxifen. Overexpressing PDE4D rescues LINC00152 inhibition-mediated tamoxifen sensitization by de-activating the cAMP/Ca2+/ferroptosis axis. Importantly, high LINC00152 expression is significantly correlated with high PDE4D/low ferroptosis and worse survival in multiple cohorts of tamoxifen- or tamoxifen-containing endocrine therapy-treated ER+ breast cancer patients. Overall, we identified LINC00152 inhibition as a novel mechanism of ferroptosis induction and tamoxifen sensitization, thereby revealing LINC00152 and its effectors as actionable therapeutic targets to improve clinical outcome in refractory ER+ breast cancer.

cancer biology↗

bioRxiv2021

The contribution of uncharted RNA sequences to tumor identity in lung adenocarcinoma

BackgroundTranscriptome analysis of cancer tissues has been instrumental in defining tumor subtypes, diagnostic signatures and cancer regulatory networks. Cancer transcriptomes are still predominantly analyzed at the level of gene expression. Few studies have addressed transcript-level variations, and most of these only looked at splice variants. Previously we introduced a k-mer based, reference-free method, DE-kupl, that performs differential analysis of RNA-seq data at the k-mer level, which enables distinguishing RNAs differing by a single nucleotide. Here we evaluate the significance of differential events discovered by this method in two independent lung adenocarcinoma RNA-seq datasets (N=583 and N=154). ResultsFocusing on differential events in a tumor vs normal setting, we found events in endogenous repeats, alternative splicing and polyadenylation sites, long non-coding RNAs, retained introns and unmapped RNAs. Replicability was highly significant for most event classes (assessed by comparing to events shared between unrelated tumors). Overall about 160,000 differential k-mer contigs were shared between datasets, including a large set of sequences from hypervariable genes such as immunoglobulins, SFTP and mucin genes. Most interestingly, we identified a set of novel tumor-specific long non-coding RNAs in intergenic and intronic regions. We found that expressed endogenous transposons defined two major groups of patients (high/low repeat expression) with distinct clinical characteristic. A number of repeats, intronic RNAs and lincRNA achieved strong patient stratification in univariate or multivariate survival models. Finally, using antigen presentation prediction, we identified 55 contigs predicted to produce recurrent tumor-specific antigens. ConclusionsK-mer based RNA-seq analysis enables description of cancer transcriptomes at nucleotide precision, independently of prior transcript annotation. Application to lung cancer data uncovered events stemming from a wide variety of transcriptional and postranscriptional mechanisms. Among those events, a significant subset was replicable between cohorts, thus constituting novel RNA hallmarks of cancer. The code is available at: https://github.com/Transipedia/dekupl-lung-cancer-inter-cohort.

bioinformatics↗

bioRxiv2021

Kmerator Suite: design of specific k-mer signatures andautomatic metadata discovery in large RNA-Seq datasets.

The huge body of publicly available RNA-seq libraries is a treasure of functional information allowing to quantify the expression of known or novel transcripts in tissues. However, transcript quantification commonly relies on alignment methods requiring a lot of computational resources and processing time, which does not scale easily to large datasets. K-mer decomposition constitutes a new way to process RNA-seq data for the identification of transcriptional signatures, as k-mers can be used to quantify accurately gene expression in a less resource-consuming way. We present the Kmerator Suite, a set of three tools designed to extract specific k-mer signatures, quantify these k-mers into RNA-seq datasets and quickly visualize large datasets characteristics. The core tool, Kmerator, produces specific k-mers for 97% of human genes, enabling the measure of gene expression with high accuracy in simulated datasets. KmerExploR, a direct application of Kmerator, uses a set of predictor genes specific k-mers to infer metadata including library protocol, sample features or contaminations from RNA-seq datasets. KmerExploR results are visualised through a user-friendly interface. Moreover, we demonstrate that the Kmerator Suite can be used for advanced queries targeting known or new biomarkers such as mutations, gene fusions or long non coding-RNAs for human health applications.

bioinformatics↗

bioRxiv2020

Identification of RNAs bound by Hfq reveals widespread RNA partners and a sporulation regulator in the human pathogen Clostridioides difficile

Noncoding RNAs (ncRNA) have emerged as important components of regulatory networks governing bacterial physiology and virulence. Previous deep-sequencing analysis identified a large diversity of ncRNAs in the human enteropathogen Clostridioides (Clostridium) difficile. Some of them are trans-encoded RNAs that could require the RNA chaperone protein Hfq for their action. Recent analysis suggested a pleiotropic role of Hfq in C. difficile with the most pronounced effect on sporulation, a key process during the infectious cycle of this pathogen. However, a global view of RNAs interacting with C. difficile Hfq is missing. In the present study, we performed RNA immunoprecipitation high-throughput sequencing (RIP-Seq) to identify Hfq-associated RNAs in C. difficile. Our work revealed a large set of Hfq-interacting mRNAs and ncRNAs, including mRNA leaders and coding regions, known and potential new ncRNAs. In addition to trans-encoded RNAs, new categories of Hfq ligands were found including cis-antisense RNAs, riboswitches and CRISPR RNAs. ncRNA-mRNA and ncRNA-ncRNA pairings were postulated through computational predictions. Investigation of one of the Hfq-associated ncRNAs, RCd1, suggests that this RNA contributes to the control of late stages of sporulation in C. difficile. Altogether, these data provide essential molecular basis for further studies of post-transcriptional regulatory network in this enteropathogen.

microbiology↗

bioRxiv2020

REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets

MotivationIn this work we present REINDEER, a novel computational method that performs indexing of sequences and records their abundances across a collection of datasets. To the best of our knowledge, other indexing methods have so far been unable to record abundances efficiently across large datasets. ResultsWe used REINDEER to index the abundances of sequences within 2,585 human RNA-seq experiments in 45 hours using only 56 GB of RAM. This makes REINDEER the first method able to record abundances at the scale of 4 billion distinct k-mers across 2,585 datasets. REINDEER also supports exact presence/absence queries of k-mers. Briefly, REINDEER constructs the compacted de Bruijn graph (DBG) of each dataset, then conceptually merges those DBGs into a single global one. Then, REINDEER constructs and indexes monotigs, which in a nutshell are groups of k-mers of similar abundances. Availabilityhttps://github.com/kamimrcht/REINDEER Contactcamille.marchet@univ-lille.fr

bioinformatics↗

bioRxiv2020

Highly reproducible characterization of Escherichia coli tRNA epitranscriptome with a simple method of library preparation for deep sequencing

In conventional RNA high-throughput sequencing, modified bases prevent a large fraction of tRNA transcripts to be converted into cDNA libraries. Recent proposals aiming at resolving this issue take advantage of the interference of base modifications with RT enzymes to detect and identify them by establishing signals from aborted cDNA transcripts. Because some modifications, such as methyl groups, do almost not allow RT bypassing, demethylation and highly processive RT enzymes have been used to overcome these obstacles. Working with Escherichia coli as a model system, we show that with a conventional (albeit still engineered) RT enzyme and key optimizations in library preparation, all RT-impairing modifications can be highlighted along the entire tRNA length without a demethylation procedure. This is achieved by combining deep-sequencing samples, which allows to establish aborted transcription signal of higher accuracy and reproducibility, with the potential for differentiating tiny differences in the state of modification of all cellular tRNAs. In addition, our protocol provides estimates of the relative tRNA abundance.

genomics↗

bioRxiv2020

TOP the Transcription Orientation Pipeline and its use to investigate the transcription of non-coding regions: assessment with CRISPR direct repeats and intergenic sequences.

A large proportion of non-coding sequences in prokaryotes are transcribed, playing an important role in the cell metabolism and defense against exogenous elements. This is the case of small RNAs and of clustered regularly interspaced short palindromic repeats "CRISPR" arrays. The CRISPR-Cas system is a defense mechanism that protects bacterial and archaeal genomes against invasions by mobile genetic elements such as viruses and plasmids. The CRISPR array, made of repeats separated by unique sequences called spacers, is transcribed but the nature of the promoter and of the transcription regulation is not well known. We describe the Transcription Orientation Pipeline (TOP) which makes use of transcriptome sequence reads to recover those corresponding to a selected sequence, and determine the direction of the transcription. CRISPR repeat sequences extracted from CRISPRCasdb were used to test the performances of the program. Statistical tests show that CRISPR elements can be reliably oriented with as little as 100 mapped reads. TOP was applied to all the available RNA-Seq Illumina sequencing archives from species possessing a CRISPR array, allowing comparisons with programs dedicated to the orientation of CRISPR repeats. In addition TOP was used to analyze small non-coding RNAs in Staphylococcus aureus, demonstrating that it is a valuable and convenient tool to investigate the transcription orientation of any sequence of interest. Availability and implementationTOPs is implemented in Python and is freely available via the I2BC github repository at https://github.com/i2bc/TOP.

bioinformatics↗

bioRxiv2019

Blind exploration of the unreferenced transcriptome reveals novel RNAs for prostate cancer diagnosis

The broad use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens is restricted by the limited power of conventional reference-based protocols relying on uniquely mapped reads and transcript annotations. Here, we implemented a blind reference-free computational protocol, DE-kupl, to directly infer RNA variations of any origin, including yet unreferenced RNAs, from high coverage total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into unannotated putative long noncoding (lnc)RNAs expressed in prostate cancer tissues. Through filtering and visual inspection of 1,179 candidates, we defined 21 lncRNA probes that were further validated for robust tumor-specific expression by NanoString single molecule-based RNA measurements in 144 tissue specimens. Predictive modeling yielded a restricted probe panel enabling over 90% of true positive detection of cancer in an independent dataset from The Cancer Genome Atlas. Remarkably, this clinical signature made of only 9 unannotated lncRNAs largely outperformed PCA3, the only RNA biomarker approved by the Food and Drug Administration agency, specifically, in detection of high-risk prostate tumors. The proposed reference-free computational workflow is modular, highly sensitive and robust and can be applied to any pathology and any clinical application.

genomics↗

bioRxiv2019

Two ecotype-related long non-coding RNAs in the environmental control of root growth

BackgroundRoot architecture varies widely between species and even between ecotypes of the same species despite the strong conservation of the protein-coding portion of their genomes. In contrast, non-coding RNAs evolved rapidly between ecotypes and may control their differential responses to the environment as several long non-coding RNAs (lncRNAs) can quantitatively regulate gene expression.\n\nResultsRoots from Columbia (Col) and Landsberg erecta (Ler) ecotypes respond differently to phosphate starvation. We compared complete transcriptomes (mRNAs, lncRNAs and small RNAs) of root tips from these two ecotypes during early phosphate starvation. We identified thousands of new lncRNAs categorized as intergenic or antisense RNAs that were largely conserved at DNA level in these ecotypes. In contrast to coding genes, many lncRNAs were specifically transcribed in one ecotype and/or differentially expressed between ecotypes independently of the phosphate condition. These ecotype-related lncRNAs were characterized by analyzing their sequence variability among plants and their link with siRNAs. Our analysis identified 675 lncRNAs differentially expressed between the two ecotypes including specific antisense RNAs targeting key regulators of root growth responses. Mis-regulation of several intergenic lncRNAs showed that at least two ecotype-related lncRNAs regulate primary root growth in Col.\n\nConclusionsThe in depth exploration of the non-coding transcriptome of two ecotypes identified thousands of new lncRNAs showing specific expression in root apexes. De-regulation of two ecotype-related lncRNAs revealed a new pathway involved in the regulation of primary root growth. The non-coding genome may reveal novel mechanisms involved in ecotype adaptation of roots to different soil environments.

plant biology↗

bioRxiv2017

Exhaustive capture of biological variation in RNA-seq data through k-mer decomposition

Each individual cell produces its own set of transcripts, which is the combined result of genetic variation, transcription regulation and post-transcriptional processing. Due to this combinatorial nature, obtaining the exhaustive set of full-length transcripts for a given species is a never-ending endeavor. Yet, each RNA deep sequencing experiment produces a variety of transcripts that depart from the reference transcriptome and should be properly identified. To address this challenge, we introduce a k-mer-based software protocol for capturing local RNA variation from a set of standard RNA-seq libraries, independently of a reference genome or transcriptome. Our software, called DE-kupl, analyzes k-mer contents and detects k-mers with differential abundance directly from the raw data files, prior to assembly or mapping. This enables to retrieve the virtually complete set of unannotated variation lying in an RNA-seq dataset. This variation is subsequently assigned to biological events such as differential lincRNAs, antisense RNAs, splice and polyadenylation variants, introns, expressed repeats, and SNV-harboring or exogenous RNA. We applied DE-kupl to public RNA-seq datasets, including an Epythelial-Mensenchymal Transition model and different human tissues. DE-kupl identified abundant novel events and showed excellent reproducibility when applied to independent deep sequencing experiments. DE-kupl is a new paradigm for analyzing differential RNA-seq data with no preconception on target events, which can also provide fresh insights into existing RNA-seq repositories.

bioinformatics↗